{"id":"W2010689979","doi":"10.1016/j.jmva.2010.01.013","title":"Generating random AR(<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si60.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>p</mml:mi></mml:math>) and MA(<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si61.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>q</mml:mi></mml:math>) Toeplitz correlation matrices","year":2010,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Toeplitz matrix; Mathematics; Autoregressive model; Invertible matrix; Series (stratigraphy); Gaussian; Matrix (chemical analysis); Discrete mathematics; Applied mathematics; Combinatorics; Pure mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00339636,0.0009483615,0.0008523478,0.001536004,0.0006260023,0.001861272,0.001981395,0.001564533,0.03288331],"category_scores_gemma":[0.02052703,0.0005300399,0.001556738,0.001904686,0.0006969752,0.002175546,0.001271071,0.002025845,0.01044418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001147541,"about_ca_system_score_gemma":0.001700694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005805317,"about_ca_topic_score_gemma":0.009052381,"domain_scores_codex":[0.9981383,0.0007419348,0.00008169159,0.0003815605,0.0004991503,0.0001572775],"domain_scores_gemma":[0.9932326,0.003648823,0.0003584374,0.001411821,0.00117361,0.000174798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000313803,0.0001582661,0.002969445,0.0001887227,0.000108548,0.0002827992,0.0002637735,0.1298223,0.003750124,0.6823693,0.03868369,0.1410892],"study_design_scores_gemma":[0.0000923093,0.00007134022,0.001384296,0.00005990297,0.00004829907,0.000252224,0.00008967614,0.7552657,0.005949649,0.219982,0.01668606,0.0001185784],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009622099,0.000105965,0.9790208,0.0004691107,0.0001655159,0.0002887635,0.001817933,0.001872491,0.00663719],"genre_scores_gemma":[0.2164305,0.0004933072,0.7239347,0.000443221,0.0001878942,0.001315576,0.007629305,0.001400458,0.048165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03288331,"threshold_uncertainty_score":0.1100057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476107882036761,"score_gpt":0.2582122102370381,"score_spread":0.2434511314166705,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}